Two great books for aspiring "data scientists"

I have two great books to recommend on two essential topics in "data science" - R statistical programming language and machine learning.

First recommended book is "The Art of R Programming" by Norman Matloff. It is a very detailed tutorial on R language with very detailed and rich explanations of data structures and functions used not only in the language, but important to know for the families of statistical analysis.

My favorite aspect of the book is the author's art of balancing coverage most of relevant subjects (including graphics, installation and object oriented programming) while not losing depth. This book is a great choice for those of us that want to get a competent at R and learn some statistics along the way.

The second book is Machine Learning for Hackers by Conway and Wright. This book is in my opinion a natural extension to the first as it features R language for all of its examples. I recommend it because it is a good survey of this extremely important but often challenging data analysis technique (machine learning). I recommend it mostly as an introductory book and if you have basic background in R and statistics.

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Blog covers everyday topics in Software Engineering and universal ones in Computational Science.
Through this blog I promote my ...
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Blog covers everyday topics in Software Engineering and universal ones in Computational Science.
Through this blog I promote my belief that even in software
"Beauty in design results from purity of function."
("Universal Principles of Design", by William Lidwell, Kritina Holden, Jill Butler, 2003, Art, pg. 90)
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